A Web Service QoS Prediction Approach Based on Collaborative Filtering

With the increasing numbers of Web services and service users on World Wide Web, predicting QoS(Quality of Service) for users will greatly aid service selection and discovery. Due to the different backgrounds and experiences of users, they have different QoS experiences when interacting with the same service. Even two users who have similar experiences on some services can have diverging views when considering other services. This paper proposes an approach to predict QoS. It is based on not only other users´ QoS experiences, but also the environment factor and user input factor. First bring forwards usage information feature model and calculate the similarity of two users based on the feature model. Then consider not only the historic information, but also environment and users´ inputs, such as bandwidth and data size. Before calculating the user similarity, select a set of Web services that have the highest degree of similarity with the target service, not all of the services. The missing value can be calculated through the data of similar services. The results of the experiment prove that our approach is feasible and effective.